Continuous Black-Box Optimization (C-BBO) benchmarks for DeepHyper.
| Function Name | Number of Dimensions | Comment |
|---|---|---|
| ackley | Many local minima and single global optimum | |
| branin | 2 | Three global optimum |
| cossin | 1 | Many local minima, good for visualisation. |
| easom | 2 | Almost flat everywhere |
| griewank | ||
| hartmann6D | 6 | |
| levy | ||
| michal | ||
| rosen | ||
| schwefel | ||
| shekel | 4 | Many local minima with flat areas |
Python installation and dependency management is handled with uv. Clone this repository then create a Python environment with uv sync.
Go to the example directory and run the benchmarks with uv run benchmark cbbo.toml. Plot the results of the benchmarks with uv run benchmark cbbo.toml --plot.